Assessing visibility of a target object with autonomous vehicle fleet

ABSTRACT

A system uses a fleet of AVs to assess visibility of target objects. Each AV has a camera for capturing images of target objects. AVs provide the captured images, or visibility data derived from the captured images, to a remote system, which aggregates visibility data describing images captured across the fleet of AVs. The AVs also provide condition data describing conditions under which the images were captured, and the remote system aggregates the condition data. The remote system processes the aggregated visibility data and condition data to determine conditions under which a target object does not meet a visibility threshold.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates generally to autonomous vehicle fleetsand, more specifically, to methods and systems for assessing visibilityof target objects using autonomous vehicle sensors.

BACKGROUND

When placing objects along roadways so that they can be viewed bytravelers, it can be challenging to account for changing conditions thataffect the visibility of objects. For example, a sign may be placed in alocation that is visible from the roadway in the winter, but by summer,the sign has been covered by foliage. Certain conditions affectingvisibility are not foreseeable; for example, scaffolding may go up andblock the view of a sign after the sign has been placed. At present,people must personally inspect placed signage to ensure continuedvisibility. Frequent inspection is inefficient, and infrequentinspection can lead to extended periods of time in which signage orother objects are not visible to travelers.

BRIEF DESCRIPTION OF THE DRAWINGS

To provide a more complete understanding of the present disclosure andfeatures and advantages thereof, reference is made to the followingdescription, taken in conjunction with the accompanying figures, whereinlike reference numerals represent like parts, in which:

FIG. 1 is a block diagram illustrating a system including a fleet ofexample autonomous vehicles (AVs) for performing visibility assessmentof target objects according to some embodiments of the presentdisclosure;

FIG. 2 illustrates an example AV assessing visibility of a target objectaccording to some embodiments of the present disclosure;

FIG. 3 illustrates an example AV sensor suite according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an onboard computer according tosome embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating a fleet management systemaccording to some embodiments of the present disclosure;

FIG. 6 illustrates a flow diagram showing a process for assessingvisibility of a target object according to some embodiments of thepresent disclosure; and

FIG. 7 illustrates a flow diagram showing a process for identifying alocation to place a target object according to some embodiments of thepresent disclosure.

DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE DISCLOSURE Overview

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for allof the desirable attributes disclosed herein. Details of one or moreimplementations of the subject matter described in this specificationare set forth in the description below and the accompanying drawings.

Many objects along a roadway are designed to be visible to peopletraveling along the roadway. Certain objects, such as stop signs andparking rule designations, have standard positions and designs. Otherobjects, including other standardized traffic signs (e.g., speed limitsigns, or highway exit markers) have standardized designs, but theirposition is discretionary. Still other signs, such as commercialadvertisements or certain municipal road signage, have discretionaryplacement and design. Roadway signage is designed to be highly visibleto drivers and passengers as they pass the signage in a car, possibly ata high speed. However, various conditions can impede the visibility ofsignage in ways that may not have been anticipated when a sign wasdesigned or placed. For example, a sign that is visible in one seasonmay be much less visible in another season, e.g., due to changingdaylight hours, changing foliage near or in front of the sign, or pollendirtying the sign during springtime. New structures, such as buildingsor scaffolding, may block view of the sign.

A fleet of autonomous vehicles (AVs) is used to continually assess thevisibility of target objects. As used herein, a target object mayinclude a road sign (e.g., a directional sign, a warning sign, aninformative sign, etc.), an advertisement (e.g., a freestandingbillboard, a sign on a side of a building, an advertisement at a busstation), a sign or banner indicating the location of a business orother public building, a roadside sculpture, or another object along theroad intended to be viewed by travelers along the road.

In some embodiments, a fleet of AVs is configured to assess thevisibility of one or more target objects. An AV may maintain a databaseof expected locations of target objects, compare its current location orplanned route to the expected locations, and determine if it is passingor about to pass the expected location of a target object. When the AVpasses an expected location of a target object, the AV captures one ormore images of the expected location, which are used to determine if thetarget object is visible. An onboard computer of the AV or a remoteserver compares each captured image to an expected image of the targetobject to determine if the target object is visible, and if so,determines at least one measurement of the visibility of the targetobject. For example, the onboard computer or remote server may determinea color contrast between different parts of the target object (e.g.,color contrast between a text color and a background color), a colorcontrast between one or more parts of the target object and itssurroundings, an amount of the target object that is visible (e.g., apercentage obstructed by another object, such as a tree or scaffolding),or an amount of text on the target object that is visible.

A remote server aggregates visibility data, such as color contrast dataand visibility percentages, from multiple AVs in the fleet. For example,during a one-week period, various AVs of the fleet may make a hundredtrips that pass by a given target object. The remote server aggregatesvisibility data for each trip along with condition data describingconditions under which the visibility data was obtained. For example,condition data includes the date, time, location of the AV, weatherconditions, and speed at which an AV drove past the target object andcaptured an image. The remote server assesses visibility of the targetobject based on the visibility data and condition data. For example, theremote server determines that a target object is highly visible duringthe daytime, but has low visibility (e.g., low observed color contrast)at nighttime. The AV fleet can continue to obtain visibility data over along period of time, such as months or years. This enables the remoteserver to identify changes in visibility that are seasonal, such aschanges due to foliage or reduced daylight hours, or changes invisibility that result from other changes in the environment, such asnew buildings or plant growth.

Using an AV fleet enables the remote server to efficiently assess thevisibility of a large number of target objects. In some embodiments, thetarget objects are passed by AVs on their usual routes, e.g., while theAVs are providing ride sharing or delivery services. This enables theAVs to collect data without making special trips that consume energy andother resources. Further, the AV fleet can capture visibility data undera wide variety of conditions, allowing the remote server to identifyvisibility patterns tied to particular conditions. The AV fleetroutinely monitors target objects, allowing the remote server to quicklyidentify when the target object has reduced visibility, e.g., due tonewly installed scaffolding or a recently downed tree.

Embodiments of the present disclosure provide a system for assessingvisibility of a target object that includes a plurality of AVs. Each AVincludes a camera, a memory, a processor, and communications circuitry.The memory is configured to store an image of a target object and anexpected location of the target object. The processor is configured tocompare a current location of the AV to the expected location of thetarget object to determine that the AV is traveling past the expectedlocation; in response, retrieve an image, captured by the camera, of afield of view comprising the expected location of the target object; andcompare the captured image to the image of the target object todetermine a visibility of the target object. The communicationscircuitry is configured to provide, to a remote system, visibility datadescribing the visibility of the target object and condition datadescribing at least one condition under which the image of the field ofview was captured. The remote system is configured to aggregate thevisibility data and the condition data received from the plurality ofAVs and, based on the aggregated visibility data, determine at least onecondition under which the target object does not meet a thresholdvisibility level.

Embodiments of the present disclosure provide a fleet management systemcomprising a vehicle manager, a database, and a visibility engine. Thevehicle manager is configured to instruct each of a plurality ofautonomous vehicles (AVs) of an AV fleet to autonomously drive past alocation of a target object, each of the plurality of AVs comprising amounted camera; and instruct each of the plurality of AVs to capture,with the camera, an image of a field of view comprising the location ofthe target object, and provide visibility data describing visibility ofthe target object in the image captured by the camera to the fleetmanagement system. The database is configured to store, for each of theplurality of AVs, the visibility data provided by the AV and associatedcondition data describing at least one condition under which each of theplurality of AVs captured its respective image. The visibility engine isconfigured to determine, based on the stored visibility data andassociated condition data, at least one condition under which the targetobject does not meet a threshold level of visibility.

Embodiments of the present disclosure provide a method for assessingvisibility of a target object, the method including instructing each ofa plurality of autonomous vehicles (AVs) of an AV fleet to autonomouslydrive past a location of a target object, each of the plurality of AVscomprising a mounted camera; instructing each of the plurality of AVs tocapture, with the camera, at least one image of a field of viewcomprising the location of the target object; receiving, from each ofthe plurality of AVs, data describing visibility of the target object inthe at least one imaged captured by the camera; aggregating visibilitydata describing, for each of the plurality of AVs, visibility of thetarget object to the camera, and associated condition data describing,for each of the plurality of AVs, at least one condition under which theAV drove past the target object; and determining, based on theaggregated visibility data and condition data, at least one conditionunder which the target object does not meet a threshold visibilitylevel.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure, in particular aspects of assessing visibility of targetobjects using an AV fleet, described herein, may be embodied in variousmanners (e.g., as a method, a system, a computer program product, or acomputer-readable storage medium). Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.” Functions described in this disclosure may beimplemented as an algorithm executed by one or more hardware processingunits, e.g. one or more microprocessors, of one or more computers. Invarious embodiments, different steps and portions of the steps of eachof the methods described herein may be performed by different processingunits. Furthermore, aspects of the present disclosure may take the formof a computer program product embodied in one or more computer-readablemedium(s), preferably non-transitory, having computer-readable programcode embodied, e.g., stored, thereon. In various embodiments, such acomputer program may, for example, be downloaded (updated) to theexisting devices and systems (e.g. to the existing perception systemdevices and/or their controllers, etc.) or be stored upon manufacturingof these devices and systems.

The following detailed description presents various descriptions ofspecific certain embodiments. However, the innovations described hereincan be embodied in a multitude of different ways, for example, asdefined and covered by the claims and/or select examples. In thefollowing description, reference is made to the drawings where likereference numerals can indicate identical or functionally similarelements. It will be understood that elements illustrated in thedrawings are not necessarily drawn to scale. Moreover, it will beunderstood that certain embodiments can include more elements thanillustrated in a drawing and/or a subset of the elements illustrated ina drawing. Further, some embodiments can incorporate any suitablecombination of features from two or more drawings.

The following disclosure describes various illustrative embodiments andexamples for implementing the features and functionality of the presentdisclosure. While particular components, arrangements, and/or featuresare described below in connection with various example embodiments,these are merely examples used to simplify the present disclosure andare not intended to be limiting. It will of course be appreciated thatin the development of any actual embodiment, numerousimplementation-specific decisions must be made to achieve thedeveloper's specific goals, including compliance with system, business,and/or legal constraints, which may vary from one implementation toanother. Moreover, it will be appreciated that, while such a developmenteffort might be complex and time-consuming; it would nevertheless be aroutine undertaking for those of ordinary skill in the art having thebenefit of this disclosure.

In the Specification, reference may be made to the spatial relationshipsbetween various components and to the spatial orientation of variousaspects of components as depicted in the attached drawings. However, aswill be recognized by those skilled in the art after a complete readingof the present disclosure, the devices, components, members,apparatuses, etc. described herein may be positioned in any desiredorientation. Thus, the use of terms such as “above”, “below”, “upper”,“lower”, “top”, “bottom”, or other similar terms to describe a spatialrelationship between various components or to describe the spatialorientation of aspects of such components, should be understood todescribe a relative relationship between the components or a spatialorientation of aspects of such components, respectively, as thecomponents described herein may be oriented in any desired direction.When used to describe a range of dimensions or other characteristics(e.g., time, pressure, temperature, length, width, etc.) of an element,operations, and/or conditions, the phrase “between X and Y” represents arange that includes X and Y.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

Other features and advantages of the disclosure will be apparent fromthe following description and the claims.

Example System for Visibility Assessment of Target Objects

FIG. 1 is a block diagram illustrating a system including a fleet ofexample autonomous vehicles (AVs) for performing visibility assessmentof target objects according to some embodiments of the presentdisclosure. The system 100 includes a fleet of AVs 110, including AV 110a, AV 110 b, and AV 110N, and a fleet management system 120. Forexample, a fleet of AVs may include a number N of AVs, e.g., AV 110 athrough AV 110N. AV 110 a includes a sensor suite 140 and an onboardcomputer 150. AVs 110 b through 110N also include the sensor suite 140and onboard computer 150. A single AV in the fleet is referred to hereinas AV 110, and the fleet of AVs is referred to collectively as AVs 110.The fleet management system 120 receives service requests for the AVs110 and dispatches the AVs 110 to carry out the service requests. Whenan AV 110 is dispatched for a service request, the fleet managementsystem 120 and/or the AV 110 may identify one or more target objectsalong the route, and the AV 110 captures one or more images includingthe target object(s). The fleet management system 120 receivesvisibility data from the AVs 110 and determines visibility for thetarget objects under various different conditions.

The AV 110 is preferably a fully autonomous automobile, but mayadditionally or alternatively be any semi-autonomous or fully autonomousvehicle; e.g., a boat, an unmanned aerial vehicle, a self-driving car,etc. Additionally, or alternatively, the AV 110 may be a vehicle thatswitches between a semi-autonomous state and a fully autonomous stateand thus, the AV may have attributes of both a semi-autonomous vehicleand a fully autonomous vehicle depending on the state of the vehicle.

The AV 110 may include a throttle interface that controls an enginethrottle, motor speed (e.g., rotational speed of electric motor), or anyother movement-enabling mechanism; a brake interface that controlsbrakes of the AV (or any other movement-retarding mechanism); and asteering interface that controls steering of the AV (e.g., by changingthe angle of wheels of the AV). The AV 110 may additionally oralternatively include interfaces for control of any other vehiclefunctions, e.g., windshield wipers, headlights, turn indicators, airconditioning, etc.

The AV 110 includes a sensor suite 140, which includes a computer vision(“CV”) system, localization sensors, and driving sensors. For example,the sensor suite 140 may include photodetectors, cameras, radar, sonar,lidar, GPS, wheel speed sensors, inertial measurement units (IMUS),accelerometers, microphones, strain gauges, pressure monitors,barometers, thermometers, altimeters, etc. The sensors may be located invarious positions in and around the AV 110. For example, the sensorsuite 140 includes multiple cameras mounted at different positions onthe AV 110.

An onboard computer 150 is connected to the sensor suite 140 andfunctions to control the AV 110 and to process sensed data from thesensor suite 140 and/or other sensors in order to determine the state ofthe AV 110. Based upon the vehicle state and programmed instructions,the onboard computer 150 modifies or controls behavior of the AV 110. Inaddition, the onboard computer 150 can instruct sensors of the sensorsuite 140 to capture particular images and/or other sensor data. Forexample, the onboard computer 150 instructs a particular camera of thesensor suite 140 to capture an image of a target object.

The onboard computer 150 is preferably a general-purpose computeradapted for I/O communication with vehicle control systems and sensorsuite 140, but may additionally or alternatively be any suitablecomputing device. The onboard computer 150 is preferably connected tothe Internet via a wireless connection (e.g., via a cellular dataconnection). Additionally or alternatively, the onboard computer 150 maybe coupled to any number of wireless or wired communication systems. Theonboard computer 150 is described further in relation to FIG. 4.

The fleet management system 120 manages the fleet of AVs 110. The fleetmanagement system 120 may manage a service that provides or uses the AVs110, e.g., a service for providing rides to users with the AVs 110, or aservice that delivers items, such as prepared foods, groceries, orpackages, using the AVs 110. The fleet management system 120 may selectan AV from the fleet of AVs 110 to perform a particular service or othertask, and instruct the selected AV (e.g., AV 110 a) to autonomouslydrive to a particular location (e.g., a delivery address). The fleetmanagement system 120 may select a route for the AV 110 to follow. Thefleet management system 120 also manages fleet maintenance tasks, suchas charging and servicing of the AVs 110.

As shown in FIG. 1, each of the AVs 110 communicates with the fleetmanagement system 120. The AVs 110 and fleet management system 120 mayconnect over a public network, such as the Internet. The fleetmanagement system 120 is described further in relation to FIG. 4.

Example AV and Target Object

FIG. 2 illustrates an example AV assessing visibility of a target objectaccording to some embodiments of the present disclosure. FIG. 2 shows anAV 210 traveling down a road 240. The AV 210 is an example of the AV 110described with respect to FIG. 1. The AV 210 includes a sensor suitesimilar to sensor suite 140; the sensor suite includes a camera 220 forcapturing images. The camera 220 has a field of view 230. In thisexample, a building 250 is located along the side of the road 240. TheAV 210 stores data describing the building 250, such as the width,height, and depth of the building, and data describing the location ofthe building. The AV 210 also stores data describing a target object260, which in this example, is a sign on a side of the building 250. Forexample, the target object 260 may be a billboard, a mural, or anothertype of sign or image. The AV 210 stores the location of the targetobject 260, e.g., data describing the position of the target objectalong the side of the building 250. The AV 210 may also store anexpected image of the target object 260.

The AV 210 determines that it is passing the target object 260 along theroad 240, and the camera 220 captures an image of the field of view 230when the field of view includes the expected location of the targetobject 260. In this example, a tree 270 is located between the AV 210and the building 250, obstructing the view of the target object 260 bythe camera 220. The camera 220 captures an image that includes the tree270 and possibly some portion of the target object 260. In someembodiments, the AV 210 retrieves the captured image of the expectedlocation of the target object and compares the captured image to astored image of the target object 260 and determines the visibility ofthe target object 260. For example, the AV 210 determines a percentageof the target object 260 that is visible, e.g., is not obscured by thetree 270. The AV 210 provides data describing the visibility of thetarget object 260 to the fleet management system 120. In someembodiments, the AV 210 alternatively or additionally provides thecaptured image to the fleet management system 120. The AV 210 alsoprovides condition data describing one or more conditions under whichthe camera 220 captured the image, such as the date and time, thelocation of the AV 210, weather information, etc. Each time the AV 210or another AV in the fleet passes the target object 260, the AV performsa similar process of capturing an image and providing visibility andcondition data to the fleet management system 120.

Example Sensor Suite

FIG. 3 illustrates an example AV sensor suite according to someembodiments of the present disclosure. The sensor suite 140 includes oneor more cameras 310, a lidar sensor 320, one or more weather sensors330, and a location sensor 340. The sensor suite 140 may have moresensors than those shown in FIG. 3, such as the sensors described withrespect to FIG. 1. In other embodiments, the sensor suite 140 may notinclude one or more sensors shown in FIG. 1. For example, the sensorsuite 140 may have a radar sensor instead of (or in addition to) thelidar sensor 320.

The cameras 310 capture images of the environment around the AV 110. Thesensor suite 140 may include multiple cameras 310 to capture differentviews, e.g., a front-facing camera, a back-facing camera, andside-facing cameras. One or more cameras 310 may be implemented using ahigh-resolution imager with a fixed mounting and field of view. One ormore cameras 310 may have adjustable field of views and/or adjustablezooms. In some embodiments, the cameras 310 capture images continuallyduring operation of the AV 110. The cameras 310 transmit the capturedimages to a perception module of the AV 110. In addition, the AV 110(e.g., the camera 310 or perception module) may route select images to aprocessor or processing module for determining visibility of targetobjects, such as target object processing module 460 described withrespect to FIG. 4.

The lidar (light detecting and ranging) sensor 320 measures distances toobjects in the vicinity of the AV 110 using reflected laser light. Thelidar sensor 320 may be a scanning lidar that provides a point-cloud ofthe region scanned. The lidar sensor 320 may have a fixed field of viewor a dynamically configurable field of view. The lidar sensor 320 mayproduce a point cloud that describes, among other things, a distance toa target object and a distance to any objects that obstruct the viewfrom the AV 110 to the target object.

The weather sensors 330 are sensors configured to measure one or moreweather conditions. For example, weather sensors 330 include atemperature sensor, an ambient light sensor, a precipitation sensor, anda barometric pressure sensor. The weather sensors 330 may gather weatherdata for various purposes, e.g., to determine whether to turn onwindshield wipers, and to determine whether to turn on headlights. Inaddition, the weather sensors 330 gather data that relates to visibilityconditions around the AV 110, such as the ambient light level in theenvironment of the AV 110, and the amount and type of precipitation inthe environment of the AV 110.

The location sensors 340 collect data that is used to determine acurrent location of the AV 110. The location sensors 340 may include aGPS sensor and one or more IMUs. The location sensors 340 may furtherinclude a processing unit (e.g., a module of the onboard computer 150,or a separate processing unit) that receives signals (e.g., GPS data andIMU data) to determine the current location of the AV 110. The locationdetermined by the location sensors 340 is used for route and maneuverplanning. The location may also be used to determine when to captureimages of the target object. The location may also be reported as acondition for the visibility data, i.e., the precise location that theimages were captured from.

Example Onboard Computer

FIG. 4 is a block diagram illustrating the onboard computer 150according to some embodiments of the present disclosure. The onboardcomputer 150 includes a map database 410, a perception module 420, aplanning module 430, an object location database 440, an object imagedatabase 450, a target object processing module 460, and communicationscircuitry 470. In alternative configurations, fewer, different and/oradditional components may be included in the onboard computer 150. Forexample, components and modules for controlling movements of the AV 110and other vehicle functions are not shown in FIG. 4. Further,functionality attributed to one component of the onboard computer 150may be accomplished by a different component included in the onboardcomputer 150 or a different system from those illustrated.

The map database 410 stores a detailed map that includes a currentenvironment of the AV 110. The map database 410 includes data describingroadways (e.g., locations of roadways, connections between roadways,roadway names, speed limits, traffic flow regulations, toll information,etc.), data describing buildings (e.g., locations of buildings, buildinggeometry, building types), and data describing other objects (e.g.,location, geometry, object type). The map database 410 may furtherinclude data describing other features, such as bike lanes, sidewalks,crosswalks, traffic lights, parking lots, etc.

The perception module 420 identifies objects in the environment of theAV 110. The sensor suite 140 produces a data set that is processed bythe perception module 420 to detect other cars, pedestrians, trees,bicycles, and objects traveling on or near the road on which the AV 110is traveling, and indications surrounding the AV 110 (such asconstruction signs, traffic cones, traffic lights, stop indicators, andother street signs). For example, the data set from the sensor suite 140may include images obtained by cameras, point clouds obtained by lidar(light detecting and ranging) sensors, and data collected by radarsensors. The perception module 420 may include one or more classifierstrained using machine learning to identify particular objects. Forexample, a multi-class classifier may be used to classify each object inthe environment of the AV 110 as one of a set of potential objects,e.g., a vehicle, a pedestrian, or a cyclist. As another example, apedestrian classifier recognizes pedestrians in the environment of theAV 110, a vehicle classifier recognizes vehicles in the environment ofthe AV 110, etc. The perception module 420 may also perform predictiveanalysis on some recognized objects, e.g., to determine projectedpathways of other vehicles, bicycles, and pedestrians.

The planning module 430 plans maneuvers for the AV 110 based on map dataretrieved from the map database 410, data received from the perceptionmodule 420, and navigation information, e.g., a route instructed by thefleet management system 120. In some embodiments, the planning module430 receives map data from the map database 410 describing known,relatively fixed features and objects in the environment of the AV 110.For example, the map data includes data describing roads as well asbuildings, bus stations, trees, fences, sidewalks, etc. The planningmodule 430 receives data from the perception module 420 describing atleast some of the features described by the map data in the environmentof the AV 110. This planning module 430 may compare map data with datafrom the perception module 420 to confirm the accuracy of the map dataand to determine the precise positions of perceived objects on the map.

The planning module 430 determines a pathway for the AV 110 to follow.When the perception module 420 detects moving objects in the environmentof the AV 110, the planning module 430 determines the pathway for the AV110 based on predicted behaviors of the objects and right-of-way rulesthat regulate behavior of vehicles, cyclists, pedestrians, or otherobjects. The pathway includes locations for the AV 110 to maneuver to,and timing and/or speed of the AV 110 in maneuvering to the locations.An AV controller (not shown in FIG. 4) instructs the movement-relatedsubsystems of the AV 110 to maneuver according to the pathway determinedby the planning module 430.

The object location database 440 stores data describing expectedlocations of target objects. The object location database 440 mayinclude data for the selected set of target objects provided by thefleet management system 120 to the AV 110. For example, the objectlocation database 440 stores data describing the locations of targetobjects along a route the AV 110 is instructed by the fleet managementsystem 120 to drive, or target objects within a city serviced by the AV110. The object location database 440 may include, for each targetobject, geographic coordinates of the target object (e.g., longitude andlatitude), an altitude (e.g., distance from ground), and size data(e.g., height and width). The object location database 440 may includedata for identifying a position in the map database 410 of the targetobject, e.g., data identifying a wall of a building in the map database410 that a target object is placed on. The fleet management system 120may transfer the expected location data over a wireless or wiredconnection, or an operator may load a physical storage medium (e.g., ahard drive) with the data onto the AV 110. The object location database440 may be periodically updated by the fleet management system 120,e.g., when new objects are added to the target object data maintained bythe fleet management system 120, or when the AV 110 travels along adifferent route or to a different region.

The object image database 450 stores images of the target objectsdescribed by the target location database. The object image database 450may store one image for each target object, e.g., an image of a signprovided by the entity that placed the sign. In some embodiments, asingle image may be associated with multiple target objects at differentlocations, e.g., if an advertiser places the image on severalbillboards. In some embodiments, the object image database 450 storesone or more images of the target object in its environment. For example,the object image database 450 stores multiple images captured underdifferent lighting conditions, during different seasons, or fromdifferent angles. The images may have been captured by the AVs 110. Insome embodiments, the object images are stored together with the objectlocations in a single target object database. The fleet managementsystem 120 may transfer the object images over a wireless or wiredconnection, or an operator may load a physical storage medium (e.g., ahard drive) with the object images onto the AV 110.

The target object processing module 460 determines to capture an imageof a target object and processes the captured image. For example, thetarget object processing module 460 determines that it is traveling pastan expected location of a target object by comparing the currentlocation of the AV 110 (e.g., as provided by the location sensors 340)to the locations of target objects in the object location database 440.The target object processing module 460 may identify target objectsalong a planned route, or portion of a planned route, for the AV 110prior to driving the route or a portion of the route by comparing theroute or route portion to the locations of the objects in the objectlocation database 440 and identifying, based on the comparison, a set ofobjects that the AV 110 plans to travel past.

In response to determining that the AV is traveling past the expectedlocation, the target object processing module 460 retrieves an imagecaptured by one of the cameras 310 of a field of view that includes theexpected location of the target object. In some embodiments, the targetobject processing module 460 identifies a particular camera thatincludes the target object in its field of view (e.g., a camera directedtowards the right side of the AV 110 if the target object is along theright side of the roadway) and requests an image or series of imagesfrom that camera, or from the perception module 420. In someembodiments, the target object processing module 460 instructs a camerato capture an image of the expected location and transmit the capturedimage to the target object processing module 460.

The target object processing module 460 compares the captured image tothe image of the target object to determine a visibility of the targetobject. In some embodiments, the target object processing module 460determines if the target object is present within the image and, if so,determines the location of the target object within the image. Forexample, the target object processing module 460 uses edge detection todetect the boundaries of the target object or a potential target objectwithin the captured image. The target object processing module 460compares the image within the boundaries to the stored image of thetarget object to determine if the target object is within the identifiedboundaries. As another example, the target object processing module 460performs divide-and-conquer search algorithm to search for the targetobject within the captured image, and identifies the boundaries of thetarget object. The target object processing module 460 may use otherobject recognition techniques, such as grayscale matching, gradientmatching, or feature-based matching, or a combination of objectrecognition techniques. As discussed above, the object image database450 may store multiple example images of the target object, e.g., imagescaptured under different lighting conditions, during different seasons,or from different angles. Comparing the captured image to multipleexample images of the target object may improve the ability of thetarget object processing module 460 to identify a target object withinthe captured image.

After finding the target object within the captured image, the targetobject processing module 460 further processes the image to determinethe visibility of the target object within the image. In one embodiment,the target object processing module 460 determines visibility of thetarget object by measuring color contrast in the image. Visibility of atarget object is greater when multiple colors within the object havegreater contrast relative to each other, e.g., when text on a signstrongly contrasts with the background of the sign. In addition,visibility of a target object is enhanced when colors within the objecthave greater contrast relative to other colors near the target object,e.g., when a sign contrasts with surrounding foliage or a buildingbehind the sign.

In one example, the target object processing module 460 identifiesboundaries of the target object in the captured image, as describedabove, and determines a color contrast between the target object and anarea outside the boundaries of the target object. The target objectprocessing module 460 may select, as a color describing the targetobject, a color near a boundary of the target object (e.g., a bordercolor or background color of a sign), the color that takes up thelargest area of the target object (e.g., the color present in thegreatest number of pixels identified as being within the boundaries ofthe target object), or an average color of the target object. The targetobject processing module 460 may select, as a color describing the areaoutside the target object, a most common color in a region near theboundary or an average color of an area outside the boundary. In someembodiments, the target object processing module 460 calculates multiplecolor contrast values, e.g., color contrasts describing each of fourboundaries (left, right, top, and bottom) of a sign.

In another example, the target object processing module 460 identifiesboundaries of the target object in the captured image, as describedabove, and determines a color contrast between different parts withinthe target object. For example, the target object processing module 460identifies texts or logos within the image of the target object, and foreach unique text or logo color, calculates a color contrast with respectto the background of the text or logo.

In another example, the target object processing module 460 identifiesboundaries of the target object in the captured image, as describedabove, and determines an obstruction level of the target object. Forexample, the target object processing module 460 compares the storedimage of the target object to the captured image of the target objectand determines an area of the target object in the captured image thatmatches the stored image, e.g., a percentage of the captured image thatmatches the stored image. The target object processing module 460 mayadjust the captured image and/or the target object so that the capturedimage and target object are the same size. For example, the targetobject processing module 460 resizes the captured image, rotates thecaptured image, and/or adjusts the perspective of the captured image.The target object processing module 460 then compares the captured imageto the stored image pixel-by-pixel to determine if each pair ofcorresponding pixels match, e.g., if the pixel colors are within athreshold color distance from each other. The pixels that do not matchmay be caused by an obstructing object, and the obstruction leveldescribes the amount of the target object that is obstructed from the AVcamera by the obstructing object.

In some embodiments, in response to determining that a target object isat least partially obstructed, the target object processing module 460identifies the location and type of the obstructing object. For example,as discussed above, the perception module 420 may include one or moreclassifiers trained using machine learning to identify particularobjects. The perception module 420 may use one or more classifierstrained to classify a particular object in the environment of the AV 110as one of a set of potential objects, e.g., a vehicle, a truck, apedestrian, a tree, scaffolding, another building, etc. The classifieror classifiers may rely on data from the lidar sensor 320, whichprovides a point cloud describing the physical shapes of threedimensional objects. In some embodiments, the classifier or classifiersrely on data from a combination of sensors, e.g., cameras 310, the lidarsensor 320, and/or radar sensors. The target object processing module460 may request, from the perception module 420, the object type for anobstructing object that was determined by the classifier or classifiers.In addition to determining the object type, the perception module 420may determine the location of the obstructing object and/or size of theobstructing object and provide this information to the target objectprocessing module 460.

In an embodiment, the target object processing module 460 determines anobstruction level for the passenger compartment of the AV 110, e.g., forthe viewpoint of a particular passenger or set of passengers of the AV.For example, if the camera 310 is on top of the AV 110, and anaverage-height passenger's eyes are located 2 feet below the camera 310,an obstructing object that obstructs a top portion of a bus station signin the field of view of the camera 310 may not obstruct the bus stationsign from the viewpoint of the passenger. To analyze the obstructionfrom the point of view of the passenger, the target object processingmodule 460 determines the locations of the target object and of theobstructing object relative to the AV 110. For example, the targetobject processing module 460 receives, from the perception module 420, adistance to the target object and a distance to the obstructing objectdetermined based on the point cloud from the lidar sensor 320. Theperception module 420 may also determine and provide data describing thesize of the target object and the size of the obstructing object, or thetarget object processing module 460 may receive the target object sizefrom the object location database 440. The target object processingmodule 460 models the relative locations of the passenger, theobstructing object, and target object based on the received location andsize data, and determines, based on the relative locations of the targetobject and obstructing object and the sizes of the target object and theobstructing object, an obstruction level of the target object from theviewpoint of the passenger.

In some embodiments, the target object processing module 460 receivesmultiple images of the target object captured at different times, as theAV 110 travels down the roadway past the target object. The targetobject processing module 460 may separately analyze each image accordingto the processes described above to determine, at different locationsalong the roadway, the visibility of the target object. In addition, thetarget object processing module 460 may determine a duration of timeand/or a portion of the roadway from which the target object is visible.For example, the target object processing module 460 determines a starttime and/or start location at which the target object becomes visiblebased on the distance to the target object, the size of the targetobject, and the relative position of the target object (e.g., whether asign is viewed straight-on or at an angle). The cameras 310 and othersensors of the AV 110 may have greater perception than an average human(e.g., a computer vision system may detect text that a person with 20/20vision cannot see), so the target object processing module 460 can beprogrammed to determine if a target object is visible to a human, e.g.,if text on a target object is readable to a human, rather than to the AV110. The target object processing module 460 also determines a stop timeand/or location at which the target object is no longer visible, anddetermines whether the target object is visible at points between thestart time and end time (or a range between the start location and endlocation), or if the target object is not visible for some period orportion of the range.

The target object processing module 460 also captures condition datadescribing conditions under which the AV 110 observed the target object.For example, condition data includes the date and time that the camera310 captured the image or images. The condition data may further includethe location of the AV 110 when each image was captured. The conditiondata may include weather condition information, as collected by theweather sensors 330. For example, the weather sensors 330 include anambient light sensor, and the condition data includes an ambient lightlevel measured by the ambient light sensor. The condition data mayinclude the speed at which the AV 110 was driving when it captured theimage. The condition data may include any other data available to the AV110 and relevant to a passenger's ability to perceive a target object.

The communications circuitry 470 communicates with the fleet managementsystem 120. In particular, the communications circuitry 470 provides, tothe fleet management system 120, the visibility data and condition datadetermined by the target object processing module 460. Thecommunications circuitry 470 also receives data and instructions fromthe fleet management system 120, such as the target object data storedin the object location database 440 and the target object images storedin the object image database 450.

In some embodiments, the communications circuitry 470 transmits thevisibility data and condition data wirelessly, e.g., over a cellularnetwork while the AV 110 is traveling, or over a local area network(e.g., a WiFi or Bluetooth connection) while the AV 110 is at an AVfacility for charging, storage, or maintenance. In some embodiments, thecommunications circuitry 470 transmits the visibility data and conditiondata over a wired connection while the AV 110 is at an AV facility. Instill other embodiments, the communications circuitry 470 transmits thevisibility data and condition data to a physical storage medium (e.g., ahard drive) that is physically removed from the AV 110 at a facility,read by another device, and transferred to the fleet management system120.

Example Fleet Management System

FIG. 5 is a block diagram illustrating the fleet management system 120according to some embodiments of the present disclosure. The fleetmanagement system 120 includes a UI (user interface) server 510, a mapdatabase 520, and a vehicle manager 530 that includes a vehicle controlmodule 540, a target object database 550, visibility database 560,condition database 570, user database 580, and a visibility engine 590.In alternative configurations, different, additional, or fewercomponents may be included in the fleet management system 120. Further,functionality attributed to one component of the fleet management system120 may be accomplished by a different component included in the fleetmanagement system 120 or a different system than those illustrated. Forexample, one or more components shown in the vehicle manager 530 mayreside outside the vehicle manager 530; e.g., the user database 580 maybe maintained and stored by the UI server 510.

The UI server 510 is configured to communicate with client devices thatprovide a user interface to users. For example, the UI server 510 may bea web server that provides a browser-based application to clientdevices, or the UI server 510 may be a mobile app server that interfaceswith a mobile app installed on client devices. The user interfaceenables the user to access a service of the fleet management system 120,e.g., to request a ride from an AV 110, or to request a delivery from anAV 110.

The map database 520 stores a detailed map describing roads and otherareas (e.g., parking lots, AV service facilities) traversed by the fleetof AVs 110. The map database 520 includes data describing roadways(e.g., locations of roadways, connections between roadways, roadwaynames, speed limits, traffic flow regulations, toll information, etc.),data describing buildings (e.g., locations of buildings, buildinggeometry, building types), and data describing other objects (e.g.,location, geometry, object type), and data describing other features,such as bike lanes, sidewalks, crosswalks, traffic lights, parking lots,etc. A portion of the data stored in the map database 520 is provided tothe AVs 110 as a map database 410, described above.

The vehicle manager 530 manages and communicates with a fleet of AVs,including AVs 110 a through 110N. The vehicle manager 530 includes avehicle control module 540, which controls the movements of the AVs 110in the fleet, and a visibility engine 590 that assesses visibility oftarget objects. The vehicle manager 530 also includes various databases,including target object database 550, visibility database 560, conditiondatabase 570, and user database 580, which are used by the visibilityengine 590 to assess visibility of target objects under differentconditions and for different passengers.

The vehicle control module 540 directs the movements of the AVs 110 inthe fleet. The vehicle control module 540 receives service requests fromusers from the UI server 510, and the vehicle control module 540 assignsservice requests to individual AVs 110. In addition, the vehicle controlmodule 540 may instruct AVs 110 to drive to other locations while notservicing a user, e.g., to improve geographic distribution of the fleet,to anticipate demand at particular locations, to drive to a chargingstation for charging, etc. In some embodiments, the vehicle controlmodule 540 instructs an AV 110 to drive to a location to capture imagesof a particular target object. The vehicle control module 540 alsoinstructs AVs 110 to return to AV facilities for recharging,maintenance, or storage. The vehicle control module 540 may include orinterface with a navigation system that selects a route for an AV 110 tofollow based on data in the map database 520.

The target object database 550 stores data describing target objects,including the locations of the target objects and expected images of thetarget objects. The target object database 550 stores the objectlocation data described with respect to the object location database 440and the object images described with respect to the object imagedatabase 450. In some embodiments, the vehicle manager 530 includes twoseparate databases for the object location data and the object imagedata rather than a single target object database 550. The target objectdatabase 550 may store data describing target objects along any roadsand other areas traversed by the fleet of AVs 110, while the objectlocation database 440 and object image database 450 for a given AV 110store a subset of the information in the target object database 550 thatdescribe target objects within a particular region or along a particularroute. As described with respect to FIG. 4, the vehicle manager 530 mayprovide data from the target object database 550, including expectedimages of target objects and expected locations of target objects, tothe AVs 110, and the AVs 110 determine visibility data of target objectsat expected locations based on the expected images.

In an embodiment, the UI server 510 receives a service request from auser, such as a request for a ride, and the vehicle control module 540selects an AV 110 of the fleet to carry out the service request. Thevehicle control module 540 identifies an origin location (e.g., acurrent location of the AV 110), a destination location (e.g., adrop-off location of the user), and any waypoints (e.g., a location topick up the user). The vehicle control module 540 determines a route forthe AV 110 based on the origin location, destination location, andwaypoints. The vehicle control module 540 may compare the determinedroute to the locations of target objects in the target object database550 to determine if the AV 110 passes the location of a target object.The vehicle control module 540 instructs the AV 110 to drive thedetermined route that includes the location of a target object, and, insome embodiments, instructs the AV 110 to capture at least one image ofthe target object(s) along the route. In other embodiments, the AV 110(e.g., the target object processing module 460) identifies targetobjects along a route or in the environment of the AV 110, as describedwith respect to FIG. 4. The vehicle control module 540 further instructsthe AV 110 to provide visibility data describing the visibility of thetarget object(s) in the image(s) captured by the AV camera (e.g.,cameras 310) to the fleet management system 120. The AV 110 determinesand provides visibility data as described with respect to FIG. 4.

In some embodiments, the vehicle control module 540 instructs an AV 110to drive to the location of a target object not along a route of the AV110. For example, the vehicle control module 540 instructs an AV 110that is not servicing user requests to drive a route that includes oneor more target objects, e.g., a target object that has not been imagedrecently, or a target object that has not been imaged in a currentcondition (e.g., at dusk, or in a rainstorm). As another example, thevehicle control module 540 identifies a target object that is near aroute determined for servicing a user, and adjusts the route of the AV110 to capture the target object. For example, the vehicle controlmodule 540 identifies a target object that adds less than a thresholdlength of time (e.g., two minutes) to a route, and routes the AV 110along the slightly longer route in order to capture the target object.

The visibility database 560 aggregates visibility data received from thefleet of AVs 110 describing the visibility of the target objects. Thevehicle manager 530 receives visibility data from the fleet of AVs 110and stores the visibility data in the visibility database 560. Thevisibility data may include any of the types of visibility datadescribed with respect to FIG. 4, including data describing colorcontrast (e.g., contrast between different parts of the target object,or contrast between the target object and a background), obstructionlevel (e.g., a percentage of the target object that is obstructed by anobstructing object, data describing the obstructing object), and/orvisibility duration (e.g., length of time the target object was visible,or a portion of the roadway from which the target object is visible).The visibility data may further include data identifying and/ordescribing any detected obstructing objects. The visibility data mayalternatively or additionally include one or more captured images. Insome embodiments, AVs 110 provide captured images, and the vehiclemanager 530 (e.g., the visibility engine 590) is configured to calculatevisibility data (e.g., color contrast, obstruction level, or visibilityduration) from the captured images, using any of the processes describedwith respect to FIG. 4.

The condition database 570 aggregates condition data received from thefleet of AVs 110 describing conditions under which the image(s) used toassess visibility were collected. The vehicle manager 530 receivescondition data from the fleet of AVs 110 and stores the condition datain the condition database 570. In some embodiments, the visibilitydatabase 560 and the condition database 570 are combined in a singledatabase. The condition data may include any of the types of conditiondata described with respect to FIG. 4, including date and time at whichthe image was captured and weather conditions under which the image wascaptured. The vehicle manager 530 may also collect data from othersources and include it in the condition database 570. For example, thecondition database 570 may include weather conditions data received froma third party weather data source.

As another example, the condition database 570 includes traffic flowdata describing traffic flow on the roadway when the AV 110 captured theimage(s). The traffic flow data may be based at least in part on datareceived from the AV 110 that provided the visibility data. In someembodiments, the vehicle manager 530 receives speed data from AVs 110across the fleet, and the vehicle manager 530 aggregates this speed datato determine real-time traffic conditions. In some embodiments, thevehicle manager 530 receives traffic flow data from third party sources.

The user database 580 stores data describing passengers traveling in AVs110 and rides taken by the passengers. The user database may storedemographic data of users of the service (e.g., a rideshare service)provided by the fleet management system 120. In some embodiments, thefleet management system 120 asks users to volunteer demographicinformation and to allow the fleet management system 120 to correlatethe demographic information with rides taken by the passengers. Thedemographic information can include, for example, age, gender, homeaddress, zip code, income, profession, and hobbies or interests. Theuser database 580 may also include ride history data for the user. Theride history data may include, for each ride, an origin location, adestination location, date and time of the ride, and data describing theroute traveled.

The visibility engine 590 is configured to process data in thevisibility database 560 and the condition database 570 to determinewhether a target object is visible, and conditions under which thetarget object is or is not visible. In a simple example, the visibilityengine 590 determines that a target object is not visible from anylocation under any condition. For example, if a building is erected thatblocks view of the target object, the visibility engine 590 maydetermine that, after a certain date, the visibility data received fromAVs 110 indicates that the target object processing module 460 did notfind the target object within the captured images. The visibility engine590 may retrieve one or more stored images of the expected location ofthe target object and confirm that the target object is not visible.Furthermore, the visibility engine 590 may determine a reason that thetarget object is not visible, e.g., that a building is blocking theexpected location of the target object. The visibility engine 590 mayreceive, from the AV 110, the output of the obstructing objectclassification performed by the onboard computer 150 (as described withrespect to FIG. 4), or the visibility engine 590 may perform obstructingobject classification on one or more received images using the processdescribed with respect to FIG. 4.

In another example, for a given target object, the visibility engine 590retrieves visibility data from the visibility database 560 andidentifies a set of instances in which the target object did not meet atleast a threshold level of visibility, e.g., a set of instances in whichthe color contrast within the target object in a captured image wasbelow a threshold contrast level. The visibility engine 590 processesthe condition data corresponding to these instances of low visibility todetermine if there is a certain condition or set of conditionscorrelated to low visibility. For example, the visibility engine 590determines that, between 15 minutes after sunset (determined based onweather data) until 8:00 pm, color contrast for a target object is belowa threshold contrast level. This may be due to lights set up toilluminate the target object automatically turning on at 8:00 pm, whichin certain times of the year is too late for the target object to becontinuously visible.

In another example, for a given target object, the visibility engine 590retrieves visibility data from the visibility database 560 that includeslocations from which the target object is and is not visible from. Forexample, each AV 110 reports visibility data based on a captured imageand a corresponding location of the AV 110 when it captured the image.As discussed above, each AV 110 may provide a range of locations fromwhich the AV 110 determined that the target object was visible. Thevisibility engine 590 processes the visibility data and associatedlocations to determine a geographic area from which the target object isvisible, e.g., a particular length of a roadway. For example, if atleast a threshold number of AVs 110 (e.g., 90%) in a given time period(e.g., the past week) determine that the target object is visible from agiven location, the visibility engine 590 determines that the targetobject is visible from this location. In some embodiments, thevisibility engine 590 also checks that the target object is visible inone or more recent captured images (e.g., four of the last five imagecaptures, including the most recent capture) from a given location indetermining that the target object is visible from that location. Thevisibility engine 590 may output map data describing visibility regionsof one or more target objects.

The visibility engine 590 can identify other trends based on correlatingvisibility data and condition data. For example, the visibility engine590 may determine if a target object often becomes obstructed duringperiods of high traffic (e.g., because another vehicle blocks the view),if a target object has reduced visibility during wind or snow, or if atarget object has reduced visibility in certain seasons or date ranges(e.g., during spring and summer due to foliage, or from April 1 throughApril 15 due to heavy pollen obstructing the target object). Theclassification of obstructing objects can be used to determine if anobstruction is expected to be permanent (e.g., a new building),semi-permanent (e.g., scaffolding or a downed tree), or temporary (e.g.,a moving vehicle). The visibility engine 590 may process the collecteddata periodically (e.g., daily or weekly) and flag any target objectswith reduced visibility, or flag changes in visibility from the priorperiod.

In some embodiments, the visibility engine 590 flags certain targetobjects that have low visibility or are not visible in captured imagesin real-time. The visibility engine 590 may implement some filters toensure that it is not flagging target objects that have already beendetermined to have reduced visibility, or flagging temporary conditionsthat resolve on their own. For example, each time new visibility data isreceived, the visibility engine 590 determines if there is an impact tovisibility (e.g., greater than 10% of the object is obstructed, or anyportion of the text is obstructed). If the visibility is reduced, thevisibility engine 590 determines if full visibility is expected underthe current set of conditions (e.g., low traffic, normal speed), or ifan expected level of visibility is met (e.g., visible but with reducedcolor contrast at nighttime). The visibility engine 590 may furtherfilter certain types of obstructions, such as moving vehicles. If thevisibility engine 590 determines that there is a new visibility impact,the visibility engine 590 transmits an alert to an operator, such as theowner of the target object. The alert may further include adetermination of the cause of the low visibility (e.g., a recentlydowned tree obstructing a portion of the target object) and/or acaptured image of the expected location of the target object. This canenable the operator to quickly address the visibility impact. In someembodiments, the target object is a dynamic sign, and an operator canquickly adjust the dynamic sign based on the visibility impact. Forexample, the operator can move the text in response to an obstruction,or adjust colors on the sign in response to a low color contrast level.

In some embodiments, the visibility engine 590 also correlatesdemographic data from the user database 580 with data from thevisibility database 560 and condition database 570. As discussed above,the visibility engine 590 determines conditions under which a giventarget object is visible, and conditions under which the given targetobject is not visible. The visibility engine 590 may determine afrequency with which a given demographic group travels past the signwhile it is expected to be visible. This can be used by an owner ormanager of one or more target objects (e.g., a set of billboards) todetermine which messages to place on which billboard to best reach atarget audience, or to set a price for a billboard based on the typesand quantities of users that pass the billboard.

Example Method for Assessing Visibility of a Target Object

FIG. 6 illustrates a flow diagram showing a process for assessingvisibility of a target object according to some embodiments of thepresent disclosure. The fleet management system 120 (e.g., the vehiclecontrol module 540) instructs 610 an AV 110 to drive past a location ofa target object. For example, the vehicle control module 540 instructsan AV 110 to drive a delivery route that includes a road that passes bya target object.

The fleet management system 120 (e.g., the vehicle control module 540)instructs 620 an AV to capture an image of the target object. Forexample, the fleet management system 120 provides the expected locationof the target object to the AV 110, which captures the image of theexpected location of the target object. As another example, the fleetmanagement system 120 provides a database of target object locations tothe AV 110 and instructions to reference the database and capture imagesof target objects when the AV 110 passes target object locations.

The fleet management system 120 receives 630 data describing visibilityof the target object in the image captured by the AV 110. For example,the fleet management system 120 receives visibility data, such as colorcontrast data or obstruction data, calculated by the AV 110 from thecaptured image. In addition or alternatively, the fleet managementsystem 120 receives the captured image, which the fleet managementsystem 120 can analyze to determine visibility of the target object.

The fleet management system 120 aggregates 640 visibility data frommultiple AVs 110 and associated condition data describing conditionsunder which the AVs 110 drove past the target object and capturedimages. The fleet management system 120 determines 650, based on theaggregated visibility data and condition data, low-visibilityconditions, e.g., a condition or set of conditions under which thetarget object does not meet a threshold visibility level.

Example Method for Identifying a Location for a Target Object

In another embodiment, a fleet of AVs 110 identifies potential locationsfor new target objects. In particular, an AV 110 can determine if a sideof a building is suitable for a sign or painting. As described withrespect to FIG. 4, the AV 110 has a map database 410 that includes thelocations and sizes of buildings. The target object processing module460 analyzes the map data to identify potential surfaces on candidatebuildings for installing a sign. If the map data includes the placementand positions of windows, doors, and other features, the AV 110 mayconsider only particular walls as potential surfaces, e.g., wallswithout windows or doors, or walls with a large area that does notinclude any doors or windows. Alternatively, the target objectprocessing module 460 can receive one or more images from the camera 310that include a potential surface and determine based on the images if asufficiently large span without any windows or doors is present on thepotential surface. The map database 410 may also include data describinga type or purpose of a building. If so, the target object processingmodule 460 may consider surfaces of certain types of buildings (e.g.,commercial buildings) while not considering surfaces of other types ofbuildings (e.g., government buildings, residential buildings) aspotential surfaces for target objects.

The target object processing module 460 analyzes captured images ofpotential surfaces to determine their visibility. The target objectprocessing module 460 may determine the visibility of the potentialsurface in the same way that the target object processing module 460determines the visibility of a target object. If the target objectprocessing module 460 determines that a potential surface has asufficiently large span on which to place a target object, and that thepotential surface is sufficiently visible (e.g., the potential surfaceis not obstructed and is visible for at least a threshold duration oftime), the target object processing module 460 provides, via thecommunications circuitry 470, data identifying the potential surface tothe fleet management system 120 as a potential location for placing atarget object.

The fleet management system 120 may aggregate data from multiple AVs 110before recommending the location as a target object. For example, if apotential surface was found to be visible to five AVs but not to tenother AVs, the surface may not be a good candidate for placing thetarget object. The fleet management system 120 may also consider trafficdata, recommending potential surfaces in high-traffic locations but notin low-traffic locations. The fleet management system 120 may furtherconsider demographic data stored in the user database 580 and reportfrequencies with which different demographic profiles drive past thepotential location.

FIG. 7 illustrates a flow diagram showing an example process foridentifying a location to place a target object according to someembodiments of the present disclosure. Camera images 710 (e.g., imagesfrom the cameras 310) and map data 720 (e.g., map data from the mapdatabase 410 describing the current environment of the AV 110) areprovided to the target object processing module 460. The target objectprocessing module 460 analyzes the images and the map data to determine730 if a potential location for a target object (e.g., a sign) isavailable in the environment. For example, the target object processingmodule 460 determines is a sufficiently large surface is presentlyvisible. If so, the target object processing module 460 determines 740if the location is visible for at least a threshold amount of time,based on the camera images 710. For example, the target objectprocessing module 460 reviews images captured over a period of at least10 seconds to determine if the location remains visible continually for10 seconds. If so, the target object processing module 460 reports 750the location of the target object to the fleet management system 120.

Select Examples

Example 1 provides a system for assessing visibility of a target object,the system including a plurality of AVs. Each AV includes a camera and amemory configured to store an image of a target object and an expectedlocation of the target object. Each AV further includes a processorconfigured to compare a current location of the AV to the expectedlocation of the target object to determine that the AV is traveling pastthe expected location; in response to determining that the AV istraveling past the expected location, retrieving an image, captured bythe camera, of a field of view including the expected location of thetarget object; and compare the captured image to the image of the targetobject to determine a visibility of the target object. Each AV furtherincludes communications circuitry configured to provide, to a remotesystem, visibility data describing the visibility of the target objectand condition data describing at least one condition under which theimage of the field of view was captured. The remote system is configuredto aggregate the visibility data and the condition data received fromthe plurality of AVs and, based on the aggregated visibility data,determine at least one condition under which the target object does notmeet a threshold visibility level.

Example 2 provides the method according to example 1, where theprocessor is configured to determine a visibility of the target objectby identifying, within the image, boundaries of the target object basedon the stored image of the target object, and determining a colorcontrast between at least a portion of the target object and an areaoutside the boundaries of the target object, the visibility dataincluding the color contrast.

Example 3 provides the method according to example 1 or 2, where theprocessor is configured to determine a visibility of the target objectby identifying, within the image, at least one boundary of the targetobject based on the stored image of the target object, and determiningan obstruction level of the target object, the obstruction leveldescribing an amount of the target object that is obstructed from the AVcamera by an obstructing object, the visibility data including theobstruction level.

Example 4 provides the method according to example 3, where each of theplurality of AVs further comprises a lidar sensor configured to capturea point cloud describing at least the target object and the obstructingobject, and the processor is further configured to classify theobstructing object as an object type of a plurality of object typesbased on the captured point cloud.

Example 5 provides the method according to example 4, where theprocessor is further configured to determine a first distance betweenthe AV and the target object based on the captured point cloud,determine a second distance between the AV and the obstructing objectbased on the captured point cloud, and determine, based on the firstdistance and the second distance, an obstruction level of the targetobject from a viewpoint of a passenger of the AV.

Example 6 provides the method according to any of the precedingexamples, where each of the plurality of AVs further comprises anambient light sensor, and the condition data comprises an ambient lightlevel measured by the ambient light sensor.

Example 7 provides the method according to any of the precedingexamples, where the processor is further configured to retrieve a seriesof images captured by the camera over time as the AV travels past theexpected location, and determine, based on the series of images, aduration of time during which the target object is visible to the AV.

Example 8 provides the method according to any of the precedingexamples, where the memory includes an image database storing images ofa plurality of objects including the target object and a locationdatabase storing a plurality of expected object locations of theplurality of objects including the expected location of the targetobject; and the processor is further configured to compare the currentlocation of the AV to the plurality of expected object locations todetermine that the AV is traveling past the expected location.

Example 9 provides a fleet management system including a vehicle managerconfigured to instruct each of a plurality of AVs of an AV fleet toautonomously drive past a location of a target object, each of theplurality of AVs including a mounted camera, and instructing each of theplurality of AVs to capture, with the camera, an image of a field ofview including the location of the target object, and provide visibilitydata describing visibility of the target object in the image captured bythe camera to the fleet management system; a database configured tostore, for each of the plurality of AVs, the visibility data provided bythe AV and associated condition data describing at least one conditionunder which each of the plurality of AVs captured its respective image;and a visibility engine configured to determine, based on the storedvisibility data and associated condition data, at least one conditionunder which the target object does not meet a threshold level ofvisibility.

Example 10 provides the system according to example 9, where the vehiclemanager is configured to, for each of the plurality of AVs, instruct theAV to drive along a route between an origin location and a destinationlocation to complete a service request, and determine that a roadwayincluded in the route passes the location of the target object.

Example 11 provides the system according to example 9 or 10, where thevisibility data includes at least one of a color contrast describing acontrast between at least a portion of the target object and an areaoutside a boundary of the target object, an obstruction level describingan amount of the target object that is obstructed from the camera of theAV by an obstructing object, and a visibility duration describing aduration of time during which the target object is visible to the cameraof the AV.

Example 12 provides the system according to any of examples 9 through11, where the condition data includes a date at which the AV capturedthe image, a time at which the AV captured the image, and datadescribing at least one weather condition under which the cameracaptured the image.

Example 13 provides the system according to any of examples 9 through12, where the fleet management system is configured to receive, from theplurality of AVs, data describing traffic flow conditions under whichthe camera captured the image, and the condition data comprises the datadescribing the traffic flow conditions.

Example 14 provides the system according to any of examples 9 through13, where the visibility engine is further configured to retrieve, fromthe database, location data describing, for each AV of the plurality ofAVs, a location from which the AV captured the image of the location ofthe target object; and determine a geographic area from which the targetobject is visible based on the location data and the visibility data.

Example 15 provides the system according to any of examples 9 through14, where the fleet management system is configured to provide, to eachof the plurality of AVs, an expected image of the target object, andeach of the plurality of AVs is configured to determine the visibilitydata describing visibility of the target object in the image captured bythe camera by comparing the image captured by the camera to the expectedimage of the target object.

Example 16 provides the system according to any of examples 9 through15, where the database is further configured to store data describing,for at least a portion of the plurality of AVs, demographic datadescribing a passenger of the AV, and the visibility engine isconfigured to determine, based on the stored visibility data anddemographic data, a frequency with which a demographic group views thetarget object.

Example 17 provides a method for assessing visibility of a targetobject, the method including instructing each of a plurality of AVs ofan AV fleet to autonomously drive past a location of a target object,each of the plurality of AVs including a mounted camera; instructingeach of the plurality of AVs to capture, with the camera, at least oneimage of a field of view including the location of the target object;receiving, from each of the plurality of AVs, data describing visibilityof the target object in the at least one imaged captured by the camera;aggregating visibility data describing, for each of the plurality ofAVs, visibility of the target object to the camera, and associatedcondition data describing, for each of the plurality of AVs, at leastone condition under which the AV drove past the target object; anddetermining, based on the aggregated visibility data and condition data,at least one condition under which the target object does not meet athreshold visibility level.

Example 18 provides the method according to example 17, whereinstructing each of the plurality of AVs to autonomously drive past thelocation of the target object includes instructing the AV to drive alonga route between an origin location and a destination location tocomplete a service request, and determining that a roadway included inthe route passes the location of the target object.

Example 19 provides the method according to example 17 or 18, where thevisibility data includes at least one of a color contrast describing acontrast between at least a portion of the target object and an areaoutside a boundary of the target object, an obstruction level describingan amount of the target object that is obstructed from the camera of theAV by an obstructing object, and a visibility duration describing aduration of time during which the target object is visible to the cameraof the AV.

Example 20 provides the method according to any of examples 17 through19, where the method further includes receiving, from each of theplurality of AVs, location data describing a location from which the AVcaptured the image of field of view comprising the location of thetarget object; and determining, based on the location data and thevisibility data, a geographic area from which the target object isvisible.

Other Implementation Notes, Variations, and Applications

It is to be understood that not necessarily all objects or advantagesmay be achieved in accordance with any particular embodiment describedherein. Thus, for example, those skilled in the art will recognize thatcertain embodiments may be configured to operate in a manner thatachieves or optimizes one advantage or group of advantages as taughtherein without necessarily achieving other objects or advantages as maybe taught or suggested herein.

In one example embodiment, any number of electrical circuits of thefigures may be implemented on a board of an associated electronicdevice. The board can be a general circuit board that can hold variouscomponents of the internal electronic system of the electronic deviceand, further, provide connectors for other peripherals. Morespecifically, the board can provide the electrical connections by whichthe other components of the system can communicate electrically. Anysuitable processors (inclusive of digital signal processors,microprocessors, supporting chipsets, etc.), computer-readablenon-transitory memory elements, etc. can be suitably coupled to theboard based on particular configuration needs, processing demands,computer designs, etc. Other components such as external storage,additional sensors, controllers for audio/video display, and peripheraldevices may be attached to the board as plug-in cards, via cables, orintegrated into the board itself. In various embodiments, thefunctionalities described herein may be implemented in emulation form assoftware or firmware running within one or more configurable (e.g.,programmable) elements arranged in a structure that supports thesefunctions. The software or firmware providing the emulation may beprovided on non-transitory computer-readable storage medium comprisinginstructions to allow a processor to carry out those functionalities.

It is also imperative to note that all of the specifications,dimensions, and relationships outlined herein (e.g., the number ofprocessors, logic operations, etc.) have only been offered for purposesof example and teaching only. Such information may be variedconsiderably without departing from the spirit of the presentdisclosure, or the scope of the appended claims. The specificationsapply only to one non-limiting example and, accordingly, they should beconstrued as such. In the foregoing description, example embodimentshave been described with reference to particular arrangements ofcomponents. Various modifications and changes may be made to suchembodiments without departing from the scope of the appended claims. Thedescription and drawings are, accordingly, to be regarded in anillustrative rather than in a restrictive sense.

Note that with the numerous examples provided herein, interaction may bedescribed in terms of two, three, four, or more components. However,this has been done for purposes of clarity and example only. It shouldbe appreciated that the system can be consolidated in any suitablemanner. Along similar design alternatives, any of the illustratedcomponents, modules, and elements of the FIGS. may be combined invarious possible configurations, all of which are clearly within thebroad scope of this Specification.

Note that in this Specification, references to various features (e.g.,elements, structures, modules, components, steps, operations,characteristics, etc.) included in “one embodiment”, “exampleembodiment”, “an embodiment”, “another embodiment”, “some embodiments”,“various embodiments”, “other embodiments”, “alternative embodiment”,and the like are intended to mean that any such features are included inone or more embodiments of the present disclosure, but may or may notnecessarily be combined in the same embodiments.

Numerous other changes, substitutions, variations, alterations, andmodifications may be ascertained to one skilled in the art and it isintended that the present disclosure encompass all such changes,substitutions, variations, alterations, and modifications as fallingwithin the scope of the appended claims. Note that all optional featuresof the systems and methods described above may also be implemented withrespect to the methods or systems described herein and specifics in theexamples may be used anywhere in one or more embodiments.

In order to assist the United States Patent and Trademark Office (USPTO)and, additionally, any readers of any patent issued on this applicationin interpreting the claims appended hereto, Applicant wishes to notethat the Applicant: (a) does not intend any of the appended claims toinvoke paragraph (f) of 35 U.S.C. Section 112 as it exists on the dateof the filing hereof unless the words “means for” or “step for” arespecifically used in the particular claims; and (b) does not intend, byany statement in the Specification, to limit this disclosure in any waythat is not otherwise reflected in the appended claims.

What is claimed is:
 1. A system for assessing visibility of a targetobject, the system comprising: a plurality of autonomous vehicles (AVs),each AV comprising: a camera; a memory configured to store an image of atarget object and an expected location of the target object; a processorconfigured to: compare a current location of the AV to the expectedlocation of the target object to determine that the AV is traveling pastthe expected location, in response to determining that the AV istraveling past the expected location, retrieving an image, captured bythe camera, of a field of view comprising the expected location of thetarget object, and compare the captured image to the image of the targetobject to determine a visibility of the target object; andcommunications circuitry configured to provide, to a remote system,visibility data describing the visibility of the target object andcondition data describing at least one condition under which the imageof the field of view was captured; wherein the remote system isconfigured to aggregate the visibility data and the condition datareceived from the plurality of AVs and, based on the aggregatedvisibility data, determine at least one condition under which the targetobject does not meet a threshold visibility level.
 2. The system ofclaim 1, wherein the processor is configured to determine a visibilityof the target object by: identifying, within the image, boundaries ofthe target object based on the stored image of the target object; anddetermining a color contrast between at least a portion of the targetobject and an area outside the boundaries of the target object, thevisibility data comprising the color contrast.
 3. The system of claim 1,wherein the processor is configured to determine a visibility of thetarget object by: identifying, within the image, at least one boundaryof the target object based on the stored image of the target object; anddetermining an obstruction level of the target object, the obstructionlevel describing an amount of the target object that is obstructed fromthe camera by an obstructing object, the visibility data comprising theobstruction level.
 4. The system of claim 3, wherein each of theplurality of AVs further comprises a lidar sensor configured to capturea point cloud describing at least the target object and the obstructingobject, wherein the processor is further configured to classify theobstructing object as an object type of a plurality of object typesbased on the captured point cloud.
 5. The system of claim 4, wherein theprocessor is further configured to: determine a first distance betweenthe AV and the target object based on the captured point cloud,determine a second distance between the AV and the obstructing objectbased on the captured point cloud, and determine, based on the firstdistance and the second distance, an obstruction level of the targetobject from a viewpoint of a passenger of the AV.
 6. The system of claim1, wherein each of the plurality of AVs further comprises an ambientlight sensor, and the condition data comprises an ambient light levelmeasured by the ambient light sensor.
 7. The system of claim 1, whereinthe processor is further configured to: retrieve a series of imagescaptured by the camera over time as the AV travels past the expectedlocation, and determine, based on the series of images, a duration oftime during which the target object is visible to the AV.
 8. The systemof claim 1, wherein the memory comprises: an image database storingimages of a plurality of objects including the target object, and alocation database storing a plurality of expected object locations ofthe plurality of objects including the expected location of the targetobject; and the processor is further configured to compare the currentlocation of the AV to the plurality of expected object locations todetermine that the AV is traveling past the expected location.
 9. Afleet management system comprising: a vehicle manager configured to:instruct each of a plurality of autonomous vehicles (AVs) of an AV fleetto autonomously drive past a location of a target object, each of theplurality of AVs comprising a mounted camera, and instruct each of theplurality of AVs to capture, with the camera, an image of a field ofview comprising the location of the target object, and providevisibility data describing visibility of the target object in the imagecaptured by the camera to the fleet management system; a databaseconfigured to store, for each of the plurality of AVs, the visibilitydata provided by the AV and associated condition data describing atleast one condition under which each of the plurality of AVs capturedits respective image; and a visibility engine configured to determine,based on the stored visibility data and associated condition data, atleast one condition under which the target object does not meet athreshold level of visibility.
 10. The system of claim 9, wherein thevehicle manager is configured to, for each of the plurality of AVs:instruct the AV to drive along a route between an origin location and adestination location to complete a service request, and determine that aroadway included in the route passes the location of the target object.11. The system of claim 9, wherein the visibility data comprises atleast one of a color contrast describing a contrast between at least aportion of the target object and an area outside a boundary of thetarget object, an obstruction level describing an amount of the targetobject that is obstructed from the camera of the AV by an obstructingobject, and a visibility duration describing a duration of time duringwhich the target object is visible to the camera of the AV.
 12. Thesystem of claim 9, wherein the condition data comprises a date at whichthe AV captured the image, a time at which the AV captured the image,and data describing at least one weather condition under which thecamera captured the image.
 13. The system of claim 9, wherein the fleetmanagement system is configured to receive, from the plurality of AVs,data describing traffic flow conditions under which the camera capturedthe image, and the condition data comprises the data describing thetraffic flow conditions.
 14. The system of claim 9, wherein thevisibility engine is further configured to: retrieve, from the database,location data describing, for each AV of the plurality of AVs, alocation from which the AV captured the image of the location of thetarget object; and determine a geographic area from which the targetobject is visible based on the location data and the visibility data.15. The system of claim 9, wherein the fleet management system isconfigured to provide, to each of the plurality of AVs, an expectedimage of the target object, wherein each of the plurality of AVs isconfigured to determine the visibility data describing visibility of thetarget object in the image captured by the camera by comparing the imagecaptured by the camera to the expected image of the target object. 16.The system of claim 9, wherein the database is further configured tostore data describing, for at least a portion of the plurality of AVs,demographic data describing a passenger of the AV, and the visibilityengine is configured to determine, based on the stored visibility dataand demographic data, a frequency with which a demographic group viewsthe target object.
 17. A method for assessing visibility of a targetobject, the method comprising: instructing each of a plurality ofautonomous vehicles (AVs) of an AV fleet to autonomously drive past alocation of a target object, each of the plurality of AVs comprising amounted camera; instructing each of the plurality of AVs to capture,with the camera, at least one image of a field of view comprising thelocation of the target object; receiving, from each of the plurality ofAVs, data describing visibility of the target object in the at least oneimaged captured by the camera; aggregating visibility data describing,for each of the plurality of AVs, visibility of the target object to thecamera, and associated condition data describing, for each of theplurality of AVs, at least one condition under which the AV drove pastthe target object; and determining, based on the aggregated visibilitydata and condition data, at least one condition under which the targetobject does not meet a threshold visibility level.
 18. The method ofclaim 17, wherein instructing each of the plurality of AVs toautonomously drive past the location of the target object comprises:instructing the AV to drive along a route between an origin location anda destination location to complete a service request, and determiningthat a roadway included in the route passes the location of the targetobject.
 19. The method of claim 17, wherein the visibility datacomprises at least one of a color contrast describing a contrast betweenat least a portion of the target object and an area outside a boundaryof the target object, an obstruction level describing an amount of thetarget object that is obstructed from the camera of the AV by anobstructing object, and a visibility duration describing a duration oftime during which the target object is visible to the camera of the AV.20. The method of claim 17, further comprising: receiving, from each ofthe plurality of AVs, location data describing a location from which theAV captured the image of field of view comprising the location of thetarget object; and determining, based on the location data and thevisibility data, a geographic area from which the target object isvisible.